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  • Key words:Vector flow fields – Oriented Textures – Optical Flow – Lie group theory – Daugman neural network  (1)
  • Neural Networks  (1)
  • 1
    Digitale Medien
    Digitale Medien
    Springer
    Neural computing & applications 6 (1997), S. 142-147 
    ISSN: 1433-3058
    Schlagwort(e): Focus of expansion ; Hough parameter plane ; Neural Networks ; Optical flow
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract In this work we consider the application context of planar passive navigation in which the visual control of locomotion requires only the direction of translation, and not the full set of motion parameters. If the temporally changing optic array is represented as a vector field of optical velocities, the vectors form a radial pattern emanating from a centre point, called the Focus of Expansion (FOE), representing the heading direction. The FOE position is independent of the distances of world surfaces, and does not require assumptions about surface shape and smoothness. We investigate the performance of an artificial neural network for the computation of the image position of the FOE of an Optical Flow (OF) field induced by an observer translation relative to a static environment. The network is characterized by a feed-forward architecture, and is trained by a standard supervised back-propagation algorithm which receives as input the pattern of points where the lines generated by 2D vectors are projected using the Hough transform. We present results obtained on a test set of synthetic noisy optical flows and on optical flows computed from real image sequences.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Machine vision and applications 10 (1997), S. 174-187 
    ISSN: 1432-1769
    Schlagwort(e): Key words:Vector flow fields – Oriented Textures – Optical Flow – Lie group theory – Daugman neural network
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract. The main goal of this paper is to describe a neural algorithm for classification and segmentation of vector flow fields. We propose to use the coefficients of their projection into an appropriate linear space as a feature vector for classification. The projection onto a suitable set of basis vectors is computed by satisfying global optimization criteria. Once the whole flow field is partitioned into a large number of small patches, two processes are performed. In the former, each small patch is classified using the associated projection coefficients estimated by using a least-square-error (LSE) technique implemented on a neural network. In the latter, segmentation into larger homogeneous regions is performed using a region growing method. Two application contexts are considered: analysis of oriented textures and 3D motion. The Lie group theory is used to identify the basis vectors suitable for defining the vector space describing the patterns of interest. In particular, the projection onto the image plane of the 3D infinitesimal generators of the 3D Euclidean group have proved to provide an effective description for the considered vector flow fields.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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